1Z0-1127 Deploying and Managing Generative AI on OCI Practice Test 4 — 15 Questions

Question 1

An administrator needs to grant a data science team access to create and manage generative AI model endpoints in a specific compartment. Which policy should they create?

Accepted Answer

Allow group DataScientists to manage generative-ai-model-family in compartment Production. Option D is correct because the verb 'manage' grants full CRUD (Create, Read, Update, Delete) permissions on the 'generative-ai-model-family' resource type, which is the specific resource family for generative AI model endpoints in OCI. This allows the DataScientists group to create and manage endpoints within the specified compartment without granting broader access to all resources.

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Allow group DataScientists to manage all-resources in compartment Production

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Allow group DataScientists to use generative-ai-model-family in compartment Production

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Allow group DataScientists to read generative-ai-model-family in compartment Production

Question 2

A company has deployed a generative AI endpoint using a custom fine-tuned model. They observe that the endpoint is returning 429 (Too Many Requests) errors during business hours. They need to handle this without losing requests. What should they implement?

Accepted Answer

Implement client-side retry with exponential backoff.. Option B is correct because implementing client-side retry with exponential backoff is the standard approach to handle HTTP 429 (Too Many Requests) errors without losing requests. When the OCI Generative AI endpoint returns a 429 status, the client can automatically retry the request after a delay that increases exponentially, reducing the load on the endpoint while ensuring all requests are eventually processed. This pattern is recommended by OCI and follows best practices for rate-limited APIs, as it allows the system to recover from transient capacity issues without manual intervention.

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Increase the endpoint's max tokens limit.

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Reduce the number of concurrent requests from the application.

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Use a dedicated AI cluster with higher capacity.

Question 3

Which TWO of the following are valid ways to consume OCI Generative AI models?

Accepted Answer

Using the OCI Console chat interface. A is correct because the OCI Console provides a built-in chat interface that allows users to interact directly with Generative AI models without writing any code. This interface is part of the OCI Generative AI service's web-based console, enabling prompt testing and model evaluation through a graphical user interface.

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Using the OCI CLI

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Using OCI SDK for Python

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Using OCI Data Flow

Question 4

Which THREE of the following are best practices when deploying a generative AI model on OCI?

Accepted Answer

Set up autoscaling for the endpoint.. Option B is correct because autoscaling ensures that the generative AI endpoint can dynamically adjust compute resources based on real-time inference traffic, maintaining low latency and high availability while optimizing cost. On OCI, autoscaling policies can be configured for dedicated AI clusters to scale the number of model serving replicas in response to metrics like CPU utilization or request queue depth.

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Store API keys in the model endpoint configuration.

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Disable logging to save costs.

Question 5

Which TWO of the following are sources of training data for fine-tuning a model in OCI Generative AI?

Accepted Answer

OCI Object Storage bucket. Option A is correct because OCI Object Storage is a supported source for training data when fine-tuning models in OCI Generative AI. The service can directly access data stored in Object Storage buckets via service-level integrations, allowing you to reference large datasets without local uploads.

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OCI File Storage

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OCI Database

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OCI Streaming

Question 6

A company has fine-tuned a custom Llama 3 model using OCI Data Science for a chatbot. They now need a production-grade inference endpoint with auto-scaling. Which OCI service should they use?

Accepted Answer

OCI Generative AI Service. Option C is correct because OCI Generative AI Service provides a fully managed, production-grade inference endpoint with built-in auto-scaling for custom models like fine-tuned Llama 3. It abstracts infrastructure management, offers serverless deployment, and integrates with OCI Data Science for model import, making it the ideal choice for a chatbot requiring scalable inference.

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OCI Functions

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OCI Data Science Model Deployment

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OCI Kubernetes Engine (OKE)

Question 7

An organization is deploying a large language model on OCI using a dedicated AI cluster. They need to minimize inference latency. Which configuration step is most critical?

Accepted Answer

Configure the cluster to use high-bandwidth RDMA networking. RDMA (Remote Direct Memory Access) bypasses the CPU and kernel to transfer data directly between GPU memories, drastically reducing latency and CPU overhead. In a dedicated AI cluster on OCI, high-bandwidth RDMA networking (e.g., using RoCE v2 or InfiniBand) is the most critical step to minimize inference latency because model parallelism and tensor parallelism across nodes depend on fast, low-latency interconnects. Without RDMA, even with optimized model parallelism, the network becomes the bottleneck, increasing per-token latency.

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Set up a load balancer across multiple regions

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Use a single VM shape to reduce network hops

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Disable model parallelism to simplify setup

Question 8

A developer is deploying a fine-tuned model using OCI Generative AI service. They want to use a custom container image for inference. Which statement is true?

Accepted Answer

You can upload a container image to OCI Container Registry and reference it when creating a dedicated AI cluster.. Option B is correct because OCI Generative AI service allows you to bring your own custom container image for inference by uploading it to OCI Container Registry (OCIR) and referencing it when creating a dedicated AI cluster. This enables you to deploy fine-tuned models with custom inference logic, dependencies, or frameworks that are not available in the built-in serving containers.

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Custom containers are only supported with OCI Data Science, not Generative AI.

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Custom containers are supported only for fine-tuning jobs, not inference.

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Custom containers are not supported; only built-in models are available.

Question 9

A company needs to ensure that only authorized users can invoke an endpoint for a generative AI model. Which OCI feature should be used to control access?

Accepted Answer

OCI Identity and Access Management (IAM) policies. OCI Identity and Access Management (IAM) policies are the correct choice because they define who (users, groups, or service principals) can invoke which OCI resources, including generative AI model endpoints. IAM policies use resource-type and verb-based statements (e.g., 'allow group A to manage ai-service-family in compartment X') to enforce authorization at the API level, ensuring only authorized principals can call the model's inference endpoint.

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Network security groups (NSGs)

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VCN flow logs

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OCI Web Application Firewall (WAF)

Question 10

A team is fine-tuning a foundation model on a large dataset stored in OCI Object Storage. They want to minimize data transfer costs. What is the best practice for locating the storage?

Accepted Answer

Place the bucket in the same region as the fine-tuning job. Option D is correct because placing the Object Storage bucket in the same OCI region as the fine-tuning job eliminates cross-region data transfer charges. OCI charges egress fees when data moves between regions, but intra-region data transfer between services in the same region is free. This minimizes costs while keeping the data accessible for the fine-tuning workload.

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Place the bucket in the same region and availability domain as the fine-tuning job

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Use OCI File Storage instead of Object Storage

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Use a cross-region bucket to leverage geographically distributed data

Question 11

During deployment of a generative AI model, the inference endpoint returns high latency and timeouts. The model is deployed on a dedicated AI cluster with multiple nodes. What is the most likely cause?

Accepted Answer

The cluster nodes are configured with insufficient parallelism or the model is not properly parallelized across nodes. High latency and timeouts in a distributed AI inference deployment typically indicate that the model workload is not efficiently distributed across the cluster nodes. Option C is correct because insufficient parallelism—either due to misconfigured node resources (e.g., insufficient vCPUs, GPU cores, or memory bandwidth) or improper model sharding/parallelization—causes some nodes to become bottlenecks while others remain underutilized, leading to queuing delays and eventual timeouts.

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The inference request batch size is too small

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The model is too large for the cluster memory

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The client-side network is slow

Question 12

A company wants to use OCI Generative AI service to generate marketing copy that adheres to brand guidelines. Which technique should they use?

Accepted Answer

Fine-tune the model with brand-specific data. Fine-tuning a pre-trained model with brand-specific data (Option D) is the correct approach because it adjusts the model's weights to align with the company's unique brand guidelines, tone, and vocabulary. This supervised learning process ensures the generated marketing copy consistently adheres to specific requirements, unlike prompt engineering which relies on ephemeral instructions that may not reliably enforce brand constraints.

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Use model distillation

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Use prompt engineering with a pre-trained model

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Use knowledge distillation

Question 13

A team has deployed a generative AI model and needs to monitor inference performance and set up alerts for increased error rates. Which OCI service should they integrate with?

Accepted Answer

OCI Monitoring. OCI Monitoring is the correct service because it provides metrics and alarms for tracking inference performance (e.g., latency, throughput) and error rates from deployed generative AI models. It allows you to set up threshold-based alerts on custom or predefined metrics, enabling proactive incident response. This directly addresses the requirement to monitor inference performance and alert on increased error rates.

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OCI Cloud Guard

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OCI Events

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OCI Logging

Question 14

An organization is deploying multiple generative AI models on a shared dedicated AI cluster. They need to isolate resource usage for each model to avoid interference. Which strategy is recommended?

Accepted Answer

Deploy each model on its own dedicated AI cluster. Option D is correct because deploying each model on its own dedicated AI cluster provides complete hardware-level isolation, ensuring that resource usage (e.g., GPU memory, compute cycles) for one model does not interfere with another. In OCI Generative AI, dedicated AI clusters are single-tenant instances, so each model gets exclusive access to its allocated infrastructure, eliminating contention. This is the recommended strategy for strict isolation in shared environments.

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Use separate fine-tuning jobs for each model

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Configure multiple virtual clusters within the dedicated AI cluster using compartment quotas

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Use OCI Resource Manager to allocate resources

Question 15

A data scientist is preparing to fine-tune a foundation model on OCI. Which two actions should they take to optimize costs? (Select TWO.)

Accepted Answer

Use the smallest model that meets accuracy requirements. Option A is correct because using the smallest model that meets accuracy requirements directly reduces the number of parameters and computational operations required during fine-tuning. On OCI, larger models consume significantly more GPU memory and compute hours, so selecting the minimal viable model minimizes both training time and associated costs. This aligns with cost optimization best practices for generative AI workloads.

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Use a single OCPU shape to minimize per-hour cost

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Use spot preemptible instances to save on compute

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Store training data in Archive Storage to reduce storage costs